Efficiency Estimation Criteria of Agro-Industrial Systems in Post-Industrial Economy
Bibliographic record
Abstract
The article reveals theoretical, methodological approaches to the selection of efficiency estimation criteria of agro-industrial systems under the conditions of post-industrial economy. Based on the provisions of system-based analysis and mechanism specialties and results of the agro-industrial production results under scientific and technological progress, the authors base primary efficiency estimation criteria selection provisions of agro-industrial systems. It has been proven, that efficiency estimation criteria of agro-industrial systems have to be developed based on such development principles as dynamics, probability, adaptability, manageability etc. The article determines and reveals basic and specific types of activities of major elements of agro-industrial systems (agriculture, stock farming, storage, processing, transportation and distribution). The article estimates the agro-industrial activity results under the conditions of postindustrial economy and particularities of their calculation. According to the authors, such results can be considered functional, economic, innovative-informational, social and ecological among others. Based on this the article studies methodological approaches to the selection of efficiency estimation criteria of agro-industrial production. This system of criteria includes basic and specific criteria, enabling to estimate its functional, economic, innovative-informational, social and ecological results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".